System and method to improve risk management in fixed odds sports betting

ABSTRACT

A system and method are configured to monitor the liability related to betting activity. A risk management system for fixed-odds sports betting comprises a plurality of data processing units being so linked as to form a tree network having a root node and at least one additional tier of tree nodes linked to the root node. The tree nodes of the lowest tier are configured to receive, store, integrate, and analyse data related to bet transactions, and the tree nodes of higher tiers, representing data aggregation nodes, are configured to receive, store, further integrate and analyse integrated data from tree nodes of lower tiers. Data related to bet transactions are integrated into a three-dimensional table, wherein the first dimension of the table relates to probability, the second dimension relates to the events offered for betting, and the third dimension relates to marks for each event offered for betting.

The present invention relates to a system and method configured to monitor the liability related to betting activity.

Betting in the context of the present application relates to placing a bet on any event, especially those in which two contestants are competing to win. The event may be a sporting event, but may just as well relate to any type of contest, such as an election, etc. or whether the sun will shine on a particular day. Different events or types of bets for the same event may be combined to the same bet.

The activity related to fixed odds sports betting represents a certain risk to the organising entity. For every event on offer for betting, the organising entity attempts to match the amount to be paid out and the amount gained for any particular outcome of an event. In return for providing this service, the organising entity receives a commission (over-round) for every bet transaction placed with said organising entity. Thus, the organising entity has to be extremely vigilant with regard to monitoring the risk it carries at any time.

One problem to be solved by the present application is therefore to provide a risk management system for fixed-odds sports betting as well as a corresponding method which provides exhaustive and immediate information as to the risk carried by an organising entity.

This problem is solved by the system according to claim 1 and the method according to claim 27. Preferred embodiments are included in the dependent claims.

The risk management system for fixed-odds sports betting according to the present invention comprises a plurality of data processing units being so linked as to form a tree network having a root node and at least one additional tier of tree nodes linked to the root node. As many additional tiers as needed may be added. The tree nodes of the lowest tier are configured to receive, store, integrate, and analyse data related to bet transactions and the tree nodes of higher tiers, representing data aggregation nodes, are configured to receive, store, further integrate and analyse integrated data from tree nodes of lower tiers. All the tree nodes except for the root node are configured to transmit integrated data to the next higher tree node in the tree network.

One advantage of said system is that all the data collected in the system can be aggregated at the root node, whereas data collected in a particular branch of the system can be aggregated at the highest node of said branch. The system thus provides for a high degree of flexibility in terms of data aggregation.

Another advantage of the said systems is that data can be analysed on every single tier and by every single tree node adding to the system's flexibility. Thus, enormous computation power and storage capacity are made available to the system and the time response of the system is increased.

Preferably, only the tree nodes of the lowest tier are configured to receive, store, integrate, and analyse data related to bet transactions.

The nodes of the lowest tier may be located in different geographical areas. Thus, a network spanning a certain region, country or even the world may be set up and integrated into a tree architecture according to the present invention. Alternatively, the nodes of the lowest tier may be allocated to different geographical areas.

Typically, the data related to bet transactions comprises, for every single bet transaction, a bet-id, a bet amount placed by the bettor, at least one event or event-id selected by the bettor, a selected result for every event or event-id, odds pertaining to the selected result (mark) for every event or event-id, and/or a win/lose flag for every event or event-id. Other information such as retailer-id, time, etc. may be included. For instance, a bettor may chose to place an amount of 100 Euro on a particular outcome of a particular football match, e.g. a win by a particular team in a national cup final. The fixed odds attributed to this result are 3/1 and the data comprises a flag field representing the actual result of the bet (win/lose) which allows the organising entity to track the result of the bet.

At least one of the tree nodes, preferably the tree nodes of the lowest tier, may be configured to attribute to the data pertaining to every single bet transaction a probability to win. The probability to win is typically calculated by forming the product of the inverse of the respective odds.

At least one of the tree nodes, preferably the tree nodes of the lowest tier, may be configured to attribute to the data pertaining to every single bet transaction a winning amount, i.e. the amount the bettor will receive in case all the betted results correspond to the actual results. The winning amount is typically calculated as the bet amount multiplied by the odds for the respective events for which the bettor betted a result.

A single bet transaction may relate to a single bet, i.e. a bet on a single event or on a combination of single events, or to a multiple bet, i.e. a combination of single, simple bets. Multiple bets are treated by decomposing it into single bets and treating the single bets accordingly.

The integrated data transmitted by any node of a particular tier may have the same format as the integrated data transmitted by any other node of said tier to ensure data integrity across the system.

At least one tree node may be configured to store a product table comprising events, event-ids, marks, i.e. possible results which may be betted on, attributed to every event, and/or odds attributed to every mark. For instance, the product table may comprise various football matches, i.e. events, for each of which a bettor may place a bet on the respective marks “win”, “draw” or “lose” with certain odds.

Said tree node may further be configured to add to the data odds pertaining to the selected result for every event or event-id for every single bet transaction. Preferably this is done at a lowest tier node.

Tree nodes may also be configured to exchange and store said product table comprising events, event-ids, marks attributed to every event, and/or odds attributed to every mark to ensure data consistency across the system.

At least one tree node, preferably at least one of the tree nodes of the lowest tier, may be configured to store data related to bet transactions in a three-dimensional table, wherein the first dimension relates to probability, the second dimension relates to the events offered for betting, and the third dimension relates to marks for each event offered for betting. The first dimension related to probability could for instance run from 1 to N where N is the number of equidistant probability intervals, the second dimension related to the events offered for betting could run from 1 to K where K is the number of events that are offered for betting, and the third dimension related to marks for each event offered for betting could run from 1 to M indicating the marks for every specific event offered for betting.

At least one tree node may further be configured to extract data from the three-dimensional table and display said data. Thus, data received by any tree node can be analysed and displayed providing flexibility as to the level at which data is being analysed.

In a preferred embodiment, at least one tree node is configured to extract two-dimensional data layers from the three-dimensional table and display said two-dimensional layers. Even lower dimensional data sets (one-/zero-dimensional) may be extracted and displayed.

Preferably, integrated data reaches the root node substantially in real-time or at least in near real-time, taking into account unavoidable processing and data transmission times, which, in turn, depend on the overall scale of the system in terms of the chosen number of nodes and tiers.

The data processing units preferably consist of computer servers or workstations with a large memory.

To ensure safe data transmission, the tree network is typically configured to ensure data security of data being transmitted in between tree nodes.

The method of integrating and analysing data related to fixed-odds sports betting according to the present invention comprises the steps of retrieving data related to bet transactions, which data includes, for every bet transaction, at least the event betted on, the selected mark, and the probability to win; and integrating said data related to bet transactions into a three-dimensional table, wherein the first dimension of the table relates to probability, the second dimension of the table relates to the events offered for betting, and the third dimension of the table relates to marks for each event offered for betting.

Preferably, integrating said data further comprises extracting a lower-dimensional data set from the data in the three-dimensional table and storing said lower-dimensional data set.

The method may further comprise the step of storing the extracted data set in a memory. The method may further comprising the step of visualising the extracted data set on a display.

Preferably, extracting and storing a lower-dimensional data set comprises adding a mark layer representing cumulated liability per event. Also, extracting and storing a lower-dimensional data set may comprise adding a probability layer representing cumulated liability per event and mark.

Preferably, the method further comprises the step of obtaining a one-dimensional array indicating the cumulated liability of bets at a given interval of probability by summing the cumulated liability per event layer along the probability axis and storing the array in memory.

The method may further comprise the step of obtaining a one-dimensional array indicating the cumulated liability per event and storing the array in memory. Also, the method may further comprise the step of obtaining a one-dimensional array indicating the cumulated liability per mark and storing the array in memory.

Finally, the method may further comprise the step of obtaining a zero-dimensional data set indicating the cumulated liability for all bets and storing the data set in memory.

The following description of an embodiment of the present invention will help clarify further aspects and details thereof and advantages pertaining thereto.

FIG. 1 shows a tree architecture according to the present invention;

FIG. 2 shows the structure of (raw) data stored in a tree node;

FIG. 3 shows an exemplary product table; and

FIG. 4 shows a cubic structure of integrated data according to the present invention.

FIG. 1 shows an embodiment of the system according to the present invention comprising a tree architecture having three tiers. Every node is formed by a workstation having a local memory large enough to store the data received by said node from the nodes in (the) lower tier(s). In the present embodiment, the tree nodes are distributed across different countries.

Every bet transaction which reaches a node of the first, lowest tier of the tree (e.g. a local broker) and is validated and accepted by the transaction system is stored locally in the work-station's memory. The structure of the locally stored data is shown in FIG. 2. It comprises a bet-id, a bet amount, a probability to win, and a winning amount, i.e. the amount to be paid out to the bettor in case the betted result and the actual result agree.

The probability to win is equal to 1/(Odds E1.s1)*1/(Odds E2.s2)* . . . *1/(Odds En.sn).

The winning amount, in turn, is equal to bet amount*(Odds E1.s1)*(Odds E2.s2)* . . . * (Odds En.sn).

The bet may be a single bet or a combination of bets. In the case of a combination of multiple bets, the workstation's memory will also store the respective event-id, bettor's selection as to the result of said event, and odds attributed to the bettor's selection. Further, a win/lose flag is stored in the memory attributed to every event-id which allows local tracking of every bet's status.

The above mentioned structure in main memory may vary depending on the particular hardware configuration and/or different approach in calculating e.g. the probabilities.

Each node of the first, lowest tier receives, analyses and stores said data before transmitting an aggregated version thereof to the node of the next higher tier to which it is linked (see FIG. 2). The node of the second tier likewise receives, analyses and stores data received from the lower nodes and transmits an aggregated version of the data to the root node (shown at the bottom of FIG. 1).

Taking into account unavoidable processing and transmission times inherent in the process of data analysis and transmission, the data from every single tree node of the lowest tier becomes more or less instantly available at the root node.

In the product table shown in FIG. 3, odds are attributed to every single mark of every single event, e.g. odds.E1.s1 regarding the first mark (e.g. loss) of the first event (e.g. national cup final). Using this table for every node, an identical data structure is guaranteed to ensure that the data from all the nodes can be aggregated and condensed into a “global” view at the root node.

At every node the stored data can be analysed, integrated, and visualised. According to the embodiment, this is done with the aid of a three-dimensional table (cubic structure), wherein the first dimension represents probability and comprises N equidistant intervals of probability between 0 and 1, the second dimension represents K events offered for betting and the third dimension represents up to M possible marks envisaged for every event. At each node, two-/one-/zero-dimensional data sets can be extracted from this cubic structure, permitting detailed analyses.

To this end, the cubic structures comprise additional layers. Firstly, an additional mark layer is added reflecting cumulated liability per event. Secondly, an additional event layer is added reflecting cumulated liability per mark. Finally, an additional probability layer is added reflecting cumulated liability per event and mark.

Further, by summing the cumulated liability per event table along the probability axis, one obtains an array of dimension N indicating the cumulated liability of bets at a given interval of probability, which is also stored in memory.

The structure is completed by adding an array for each additional layer, i.e. cumulated liability per event and cumulated liability per mark. Finally, a single cell is added summarising the liability of all bets. The full structure is schematically shown in FIG. 4.

According to the calculated value “probability to win” the probability layer is identified. By dividing the betting amount and the winning amount by the number of selections for the specific bet, obtaining the betting amount per selection and the winning amount per selection and for each selection the event layer and the mark layer are identified. For each selection the three coordinates are used (probability layer, event layer, and mark layer). The values of the winning amount per selection are summed to the existing value of identified cell in the cubic structure. At the same time, calculations are performed in order to obtain the summary values to the extension layers of the cubic structure, to the two arrays and to the summary cell.

This structure is updated any time a new bet is accepted by the transaction engine and it is transmitted to the higher level in the tree structure when it is requested. The higher level node which has requested the summary information from all lower nodes to which it is connected will merge the results by simply adding the values of each cell of different cubic structure in a local cubic cell.

Two more cubic cells are created in memory to store the information related to income and over-round values by following the same procedure.

In this way, one arrives at homogeneous information on the betting activity on all nodes of the tree structure. This information may be used to monitor the betting activity by using graphic representations of the results, such as the betting behaviour of the bettors, scenarios that might lead to maximum/minimum liability, etc.

It is possible to use additional structures to store different kinds of information and display them in the same way as the liability (e.g. bet amount etc.)

Other embodiments are within the scope of the present invention. 

1. A risk management system for fixed-odds sports betting, comprising: a plurality of data processing units being so linked as to form a tree network having a root node and at least one additional tier of tree nodes linked to the root node; wherein tree nodes of the lowest tier are configured to receive, store, integrate, and analyse data related to bet transactions; wherein tree nodes of higher tiers represent data aggregation nodes configured to receive, store, further integrate and analyse integrated data from tree nodes of lower tiers; and wherein all the tree nodes except for the root node are configured to transmit integrated data to the next higher tree node in the tree network.
 2. The system according to claim 1, wherein tree nodes of the lowest tier are located in or allocated to different geographical areas.
 3. The system according to claim 1, wherein the data related to bet transactions comprises, for every single bet transaction, a bet-id.
 4. The system according to claim 1, wherein the data related to bet transactions comprises, for every single bet transaction, a bet amount.
 5. The system according to claim 1, wherein the data related to bet transactions comprises, for every single bet transaction, at least one event or event-id.
 6. The system according to claim 5, wherein the data related to bet transactions comprises, for every single bet transaction, a selected mark for every event or event-id.
 7. The system according to claim 6, wherein the data related to bet transactions comprises, for every single bet transaction, odds pertaining to the selected mark for every event or event-id.
 8. The system according to claim 5, wherein the data related to bet transactions comprises, for every single bet transaction, a win/lose flag for every event or event-id.
 9. The system according to claim 1, wherein at least one of the tree nodes is configured to attribute to the data pertaining to every single bet transaction a probability to win.
 10. The system according to claim 1, wherein at least one of the tree nodes is configured to attribute to the data pertaining to every single bet transaction a winning amount.
 11. The system according to claim 1, wherein every single bet transaction comprises a single bet or a multiple bet, and wherein the multiple bets are decomposed into single bets.
 12. The system according to claim 1, wherein at least one tree node is configured to store a product table comprising events, event-ids, marks attributed to every event, and/or odds attributed to every mark, wherein said tree node is further configured to assign odds to the data pertaining to the selected result for every event or event-id for every single bet transaction.
 13. The system according to claim 1, wherein tree nodes are configured to exchange and store a product table comprising events, event-ids, marks attributed to every event, and/or odds attributed to every mark.
 14. The system according to claim 1, wherein the integrated data transmitted by any node of a particular tier has the same format as the integrated data transmitted by any other node of said tier.
 15. The system according to claim 1, wherein at least one tree node is configured to integrate and store data related to bet transactions into a three-dimensional table, wherein the first dimension relates to probability, the second dimension relates to the events offered for betting, and the third dimension relates to marks for each event offered for betting.
 16. The system according to claim 15, wherein the first dimension related to probability runs from 1 to N where N is the number of equidistant probability intervals.
 17. The system according to claim 15, wherein the second dimension related to the events offered for betting runs from 1 to K where K is the number of events that are offered for betting.
 18. The system according to claim 15, wherein the third dimension related to marks for each event offered for betting runs from 1 to M indicating the marks for every specific event offered for betting.
 19. The system according to claim 15, wherein at least one tree node is configured to analyse and/or extract data from the three-dimensional table and display said data.
 20. The system according to claim 15, wherein at least one tree node is configured to extract two-dimensional data sets from the three-dimensional table.
 21. The system according to claim 15, wherein at least one tree node is configured to extract one-dimensional data sets from the three-dimensional table.
 22. The system according to claim 15, wherein at least one tree node is configured to extract zero-dimensional data sets from the three-dimensional table.
 23. The system according to claim 15, wherein at least one tree node is configured to display data sets extracted from said three-dimensional table.
 24. The system according to claim 1, wherein the integrated data reaches the root node substantially in real-time.
 25. The system according to claim 1, wherein the data processing units comprise computer servers or workstations.
 26. The system according to claim 1, wherein the tree network is configured to ensure data security of data being transmitted in between tree nodes.
 27. A method of integrating and analysing data related to fixed-odds sports betting, the method comprising: retrieving data related to bet transactions, which data includes, for every bet transaction, at least an event betted on, a selected mark, and a probability to win; integrating said data related to bet transactions into a three-dimensional table, wherein the first dimension of the table relates to probability, the second dimension of the table relates to the events offered for betting, and the third dimension of the table relates to marks for each event offered for betting.
 28. The method according to claim 27, wherein integrating said data further comprises extracting a lower-dimensional data set from the data in the three-dimensional table and storing said lower-dimensional data set.
 29. The method according to claim 28, further comprising storing the extracted data set in a memory.
 30. The method according to claim 28, further comprising visualising the extracted data set on a display.
 31. The method according to claim 28, wherein extracting and storing a lower-dimensional data set comprises adding a mark layer representing a cumulated liability per event.
 32. The method according to claim 31, further comprising: obtaining a one-dimensional array indicating the cumulated liability of bets at a given interval of probability by summing the cumulated liability per event layer along a probability axis and storing the array in memory.
 33. The method according to claim 28, wherein extracting and storing a lower-dimensional data set comprises adding a probability layer representing a cumulated liability per event and mark.
 34. The method according to claim 33, further comprising: obtaining a one-dimensional array indicating the cumulated liability per event and storing the array in memory.
 35. The method according to claim 33, further comprising: obtaining a one-dimensional array indicating the cumulated liability per mark and storing the array in memory.
 36. The method according to claim 27, further comprising: obtaining a zero-dimensional data set indicating a cumulated liability for all bets and storing the data set in memory. 